Azure Data Factory vs Hevo comparison

Why PeerSpot?

Microsoft and Hevo Data are both solutions in the Data Integration category. Microsoft is ranked #6 with an average rating of 8.0, while Hevo Data is ranked #47 with an average rating of 8.0. Microsoft holds a 2.2% mindshare in DI, compared to Hevo Data’s 0.5% mindshare. Additionally, 92% of Microsoft users are willing to recommend the solution, compared to 100% of Hevo Data users who would recommend it.

Azure Data Factory

Read 97 Azure Data Factory reviews

16,078 Views
11,255 Comparison Views

92% willing to recommend

Hevo

Read 2 Hevo reviews

1,463 Views
1,448 Comparison Views

100% willing to recommend

Azure Data Factory

Hevo

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between Azure Data Factory and Hevo based on real PeerSpot user reviews.

Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Azure Data Factory vs. Hevo Report (Updated: November 2026).

Buyer's Guide

Azure Data Factory vs. Hevo

November 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Azure Data Factory

Ranking in Data Integration

6th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Cloud Data Warehouse (8th)

Hevo

Ranking in Data Integration

47th

Average Rating

8.0

Reviews Sentiment

2.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2026, in the Data Integration category, the mindshare of Azure Data Factory is 2.2%, down from 4.4% compared to the previous year. The mindshare of Hevo is 0.5%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Integration Mindshare Distribution
Product	Mindshare (%)
Azure Data Factory	2.2%
Hevo	0.5%
Other	97.3%

Data Integration

Featured Reviews

Kunal Das

Test Engineer at Happiest Minds Technologies

Drag-and-drop pipelines have saved days of work and now automate data movement and backfilling

If the AI features were more improved so that I don't have to provide each and every detail, Azure Data Factory could be improved in a much better way by improving the AI features. For example, if I want to fetch any data from a raw source, I need to provide each and every detail. But if I am just uploading my raw data and if AI will sync with that data, it can analyze that data and give me proper suggestions on how that should be done in a proper way. Automatic suggestions could improve in a much better way. As I have mentioned, the AI features as well as more drag-and-drop activities could be improved. If I am making a pipeline, it should give me suggestions, such as which activity should be used, so that I don't have to remember each activity. If I have used one activity, I shouldn't have to remember what activity should I use next. It should give auto-suggestions. That is why I have given a nine out of 10. Currently, I don't know about its governance and security, but in view of its improvement, I think Azure Data Factory should improve in these areas. As I already mentioned, the AI features should be improved. Also, the auto-suggestion features should also improve.

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davidmwilliams

CIO / CTO / Head Of Data at Data Excellence

Centralizes customer data for fast analytics but has struggled with many concurrent data pipelines

Hevo could be improved as we hit limits, particularly when it couldn't keep up with the high number of databases we wanted to sync frequently—ideally updating everything hourly for near real-time analytics rather than a day behind. We found that with many pipelines, performance degraded. After discussing with Hevo engineers, I learned that Hevo is designed for fast individual pipelines, but struggles with many concurrent pipelines. They suggested that consolidating data from fewer pipelines with more fields would perform better, but even that failed when I tested it with a Business Central database that had numerous fields. Hevo does have limits on the number of pipelines it can manage concurrently and on the number of fields in a single pipeline.Additionally, the API was slower to release compared to the user interface and was not feature-complete, which was frustrating. That said, Hevo actively worked on bridging many gaps. Overall, these were significant pain points I encountered.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."

"It works very well with Azure Data Factory to pull the records, parse them quickly and post them in the database and data warehouse."

"It's extremely consistent."

"The most valuable features are data transformations."

"The overall performance is quite good."

"The most valuable aspect is the copy capability."

"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"We haven't had any issues connecting it to other products."

More Azure Data Factory pros

"Hevo positively impacted my organization by allowing us to move data quickly and easily, enabling us to focus on our real challenge: performing analytics and reporting out of Snowflake with all customer data centralized despite using various applications."

"Hevo has positively impacted my organization by ensuring excellent usage of collected data, which is utilized in various workflows, including sales and marketing and customer relationship management, ultimately helping me serve clients better."

Cons

"I have not found any real shortcomings within the product."

"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."

"It can improve from the perspective of active logging. It can provide active logging information."

"The initial setup is not very straightforward."

"When the record fails, it's tough to identify and log."

"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"There are limitations when processing more than one GD file."

More Azure Data Factory cons

"There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-established."

"Hevo could be improved as we hit limits, particularly when it couldn't keep up with the high number of databases we wanted to sync frequently—ideally updating everything hourly for near real-time analytics rather than a day behind."

Pricing and Cost Advice

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

"Data Factory is affordable."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"I would rate Data Factory's pricing nine out of ten."

"ADF is cheaper compared to AWS."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"The price is fair."

"The cost is based on the amount of data sets that we are ingesting."

More Azure Data Factory pricing and cost advice

Information not available

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Top Industries

By visitors reading reviews

Manufacturing Company

11%

Financial Services Firm

10%

Outsourcing Company

Computer Software Company

Comms Service Provider

15%

Construction Company

11%

Security Firm

11%

Retailer

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	31
Midsize Enterprise	21
Large Enterprise	64

No data available

Questions from the Community

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

How does Azure Data Factory compare with Informatica PowerCenter?

Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up an...

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How does Azure Data Factory compare with Informatica Cloud Data Integration?

Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power Q...

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What needs improvement with Hevo?

There are areas where Hevo could improve, particularly regarding its cost structure, as the pricing is based on usage and the advanced features can become costly for startups that are not well-esta...

See all answers

What is your primary use case for Hevo?

Hevo is mainly used for data integration purposes, and I use it to form a pipeline or a gateway where data is collected from various data collection platforms or sources. Hevo is a data integration...

See all answers

What advice do you have for others considering Hevo?

I would rate this product highly based on my experience with it.

See all answers

Comparisons

Snowflake vs Azure Data Factory

Compared 4% of the time

Databricks vs Azure Data Factory

Compared 4% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Azure Data Factory

Compared 3% of the time

Palantir Foundry vs Azure Data Factory

Compared 3% of the time

SnapLogic vs Azure Data Factory

Compared 3% of the time

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Fivetran vs Hevo

Compared 22% of the time

Stratio Generative AI Data Fabric vs Hevo

Compared 22% of the time

Talend Data Fabric vs Hevo

Compared 22% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs Hevo

Compared 10% of the time

Estuary vs Hevo

Compared 4% of the time

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Product Reports

Buyer's Guide

Azure Data Factory

October 2026

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Buyer's Guide

Data Integration

October 2026

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Overview

Azure Data Factory efficiently manages and integrates data from various sources, enabling seamless movement and transformation across platforms. Its valuable features include seamless integration with Azure services, handling large data volumes, flexible transformation, user-friendly interface, extensive connectors, and scalability. Users have experienced improved team performance, workflow simplification, enhanced collaboration, streamlined processes, and boosted productivity.

Microsoft

Hevo is an intuitive data integration platform designed to streamline real-time data collection and analysis for businesses. It offers a seamless way to automate data pipelines, eliminating the complexities associated with data management and integration.

Hevo provides an efficient data integration service that enables organizations to connect data from a range of sources into a unified destination for better analysis and decision-making. By supporting real-time data processing, Hevo helps businesses maintain updated analytics, ensuring they can quickly react to changing trends. With its no-code platform, it helps reduce dependency on IT teams, accelerating the integration process and facilitating quicker insights. This platform is suitable for businesses looking to simplify their data workflows and empower their analytics teams.

What are the key features of Hevo?

No-Code Data Pipelines: Build without technical expertise, enabling faster deployment of workflows.
Real-Time Data Processing: Ensure timely analytics to support immediate data-driven decisions.
Seamless Integrations: Connect with a broad range of data sources effortlessly.
Automated Schema Management: Adjust to changes in data walkthrough automation, reducing manual interventions.

What benefits should users expect from using Hevo?

Improved Efficiency: Automation reduces manual tasks, freeing up resources for strategic initiatives.
Enhanced Agility: Adapt swiftly to data changes, improving response times to market dynamics.
Cost Savings: Minimized need for technical resources lowers operational expenses.
Scalable Solutions: Flexible infrastructure supports growth without compromising performance.

Hevo is widely adopted across industries such as e-commerce, finance, and healthcare, where it facilitates efficient data integration and analytics. In e-commerce, Hevo aids in real-time inventory and customer insights. Financial sectors leverage it for fraud detection and compliance analysis, while healthcare providers use it to integrate patient data for improved outcomes.

Hevo Data

Sample Customers

1. Adobe 2. BMW 3. Coca-Cola 4. General Electric 5. Johnson & Johnson 6. LinkedIn 7. Mastercard 8. Nestle 9. Pfizer 10. Samsung 11. Siemens 12. Toyota 13. Unilever 14. Verizon 15. Walmart 16. Accenture 17. American Express 18. AT&T 19. Bank of America 20. Cisco 21. Deloitte 22. ExxonMobil 23. Ford 24. General Motors 25. IBM 26. JPMorgan Chase 27. Microsoft (Azure Data Factory is developed by Microsoft) 28. Oracle 29. Procter & Gamble 30. Salesforce 31. Shell 32. Visa

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Buyer's Guide

Azure Data Factory vs. Hevo

November 2026

Free Report: Azure Data Factory vs. Hevo

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